Department of Physics and Astronomy, Department of Biochemistry, Dalton Cardiovascular Research Center, Informatics Institute, University of Missouri, Columbia, Missouri, 65211.
Proteins. 2013 Dec;81(12):2183-91. doi: 10.1002/prot.24435. Epub 2013 Nov 14.
Inclusion of entropy is important and challenging for protein-protein binding prediction. Here, we present a statistical mechanics-based approach to empirically consider the effect of orientational entropy. Specifically, we globally sample the possible binding orientations based on a simple shape-complementarity scoring function using an FFT-type docking method. Then, for each generated orientation, we calculate the probability through the partition function of the ensemble of accessible states, which are assumed to be represented by the set of nearby binding modes. For each mode, the interaction energy is calculated using our ITScorePP scoring function that was developed in our laboratory based on principles of statistical mechanics. Using the above protocol, we present the results of our participation in Rounds 22-27 of the Critical Assessment of PRedicted Interactions (CAPRI) experiment for 10 targets (T46-T58). Additional experimental information, such as low-resolution small-angle X-ray scattering data, was used when available. In the prediction (or docking) experiments of the 10 target complexes, we achieved correct binding modes for six targets: one with high accuracy (T47), two with medium accuracy (T48 and T57), and three with acceptable accuracy (T49, T50, and T58). In the scoring experiments of seven target complexes, we obtained correct binding modes for six targets: one with high accuracy (T47), two with medium accuracy (T49 and T50), and three with acceptable accuracy (T46, T51, and T53).
纳入熵对于蛋白质-蛋白质结合预测很重要且具有挑战性。在这里,我们提出了一种基于统计力学的方法,可以从经验上考虑取向熵的影响。具体来说,我们根据基于形状互补性评分函数的简单方法,使用 FFT 类型的对接方法全局采样可能的结合方向。然后,对于每个生成的方向,我们通过可及状态的系综的配分函数计算概率,假设这些状态由附近的结合模式集表示。对于每个模式,使用我们在实验室中基于统计力学原理开发的 ITScorePP 评分函数计算相互作用能。使用上述方案,我们介绍了我们在第 22-27 轮 Critical Assessment of PRedicted Interactions (CAPRI) 实验中针对 10 个靶标 (T46-T58) 的结果。当有可用的额外实验信息(如低分辨率小角 X 射线散射数据)时,将其用于实验。在 10 个靶标复合物的预测(或对接)实验中,我们对六个靶标实现了正确的结合模式:一个具有高精度 (T47),两个具有中等精度 (T48 和 T57),三个具有可接受精度 (T49、T50 和 T58)。在七个靶标复合物的评分实验中,我们获得了六个靶标正确的结合模式:一个具有高精度 (T47),两个具有中等精度 (T49 和 T50),三个具有可接受精度 (T46、T51 和 T53)。